Assessing the performance of educational institutions: A multidimensional approach

Triperina, Evangelia; Sgouropoulou, Cleo; Xydas, Ioannis; Terraz, Olivier; Miaoulis, Georgios

doi:10.1109/educon.2017.7943021

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…For these reasons, other studies seek alternatives to build rankings of universities; the Leiden Ranking is one example [10]. Hence, different measuring methods have been proposed, including a model based on the partial sum of quadratic differences [11], multidimensional analysis [12] and models based on fuzzy logic [13], [14].…”

Section: Rankingsmentioning

confidence: 99%

Rankings analysis with the Optimized Pareto method.

Saavedra

Garcia-Baños

Bucheli

2020

AiBi Revista de Investigación, Administración e Ingeniería

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Rankings compare the performance of organizations. In many cases, rankings provide a good assessment of successful or-ganizations. However, rankings often generate controversy and debate since they support the making decisions. A ranking is a weighted linear combination of indicators, and the weights assigned to each of the indicators can lead to different rank orders. In most cases, rankings are used as a tool to support making decisions, such as resource allocation; therefore, these decisions can be affected by the assignment of such weights. In this article, we analyze the behavior of a ranking and the weights; simulations are used to calculate the change in the order of the equally weighted ranking and of the randomly weighted ranking. In this regard, we present a discussion and ranking design alternatives.

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Section: Rankingsmentioning

confidence: 99%

Rankings analysis with the Optimized Pareto method.

Saavedra

Garcia-Baños

Bucheli

2020

AiBi Revista de Investigación, Administración e Ingeniería

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“…The proposed multidimensional ontology-based visual ranking (MOBVR) framework respects the diversity of prospective end-users, supporting them throughout the entire decision-making process. Its application entails the MOBVR domain competency check (Triperina et al, 2017), a set of requirements that ensures the domain-framework compatibility. The competency check involves three categories of requirements: the first examines the framework applicability to the domain; the second consists of the algorithm's prerequisites for the domain, while the third retrieves key information for applying the framework to the selected domain, e.g.…”

Section: The Multidimensional Ontology-based Visual Ranking Frameworkmentioning

confidence: 99%

Visual-aided ontology-based ranking on multidimensional data: a case study in academia

Triperina

Bardis

Sgouropoulou

et al. 2018

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Purpose The purpose of this paper is to introduce a novel framework for visual-aided ontology-based multidimensional ranking and to demonstrate a case study in the academic domain. Design/methodology/approach The paper presents a method for adapting semantic web technologies on multiple criteria decision-making algorithms to endow to them dynamic characteristics. It also showcases the enhancement of the decision-making process by visual analytics. Findings The semantic enhanced ranking method enables the reproducibility and transparency of ranking results, while the visual representation of this information further benefits decision makers into making well-informed and insightful deductions about the problem. Research limitations/implications This approach is suitable for application domains that are ranked on the basis of multiple criteria. Originality/value The discussed approach provides a dynamic ranking methodology, instead of focusing only on one application field, or one multiple criteria decision-making method. It proposes a framework that allows integration of multidimensional, domain-specific information and produces complex ranking results in both textual and visual form.

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“…Figure 13 -Flowchart of ranking a multidimensional domain [293] Based on the proposed methodology, the MOBVR system (Figure 14) has been developed, in which the intersection of techniques from different research areas is examined and implemented. The framework is built to support ranking and comprises the competency check, the data layer, the ontology layer, the dynamic MCDM layer, the interactive presentation layer, as well as the reusability layer.…”

Section: Methodsmentioning

confidence: 99%

Visual interactive knowledge management for multicriteria decision making and ranking in linked open data environments

Τριπερίνα

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Η διδακτορική διατριβή αφορά στην εφαρμογή της οπτικής αναλυτικής και των τεχνολογιών σημασιολογικού ιστού στην πολυκριτηριακή λήψη αποφάσεων και πιο συγκεκριμένα σε προβλήματα κατάταξης εναλλακτικών, στο πλαίσιο των ερευνητικών και ακαδημαϊκών πληροφοριακών συστημάτων. Οι οπτικοποιήσεις υποστηρίζονται επίσης από διαδικασίες εξόρυξης δεδομένων και εξαγωγής γνώσης σε περιβάλλοντα διασυνδεδεμένων δεδομένων. Επιπροσθέτως, θα χρησιμοποιηθούν τεχνικές οπτικής αναλυτικής για την οργάνωση των οπτικοποιήσεων, προκειμένου να παρουσιαστούν οι πληροφορίες με τέτοιο τρόπο που θα αξιοποιηθούν οι αντιληπτικές ικανότητες των χρηστών και τελικά θα ενισχύσουν τις διαδικασίες υποστήριξης αποφάσεων και χάραξης πολιτικής. Επιπλέον, η οπτική αναπαράσταση και κατά συνέπεια οι διαδικασίες λήψης αποφάσεων και χάραξης πολιτικής θα βελτιωθούν μέσω των σημασιολογικών τεχνολογιών που βασίζονται σε εννοιολογικά μοντέλα με τη μορφή οντολογιών. Έτσι, ο κύριος στόχος της παρούσας διδακτορικής διατριβής είναι ο συνδυασμός των σημασιολογικών τεχνολογιών με τεχνικές διαδραστικών οπτικοποιήσεων, προκειμένου να γίνουν πιο αποτελεσματικά τα συστήματα υποστήριξης αποφάσεων. Κατά την διάρκεια του διδακτορικού αναπτύχθηκε ένα πολυδιάστατο πλαίσιο βασισμένο σε οντολογίες για οπτική κατάταξη (MOBVR framework), το οποίο το εφαρμόστηκε σε δυο μελέτες περίπτωσης, η πρώτη ήταν ο ακαδημαϊκός τομέας και η δεύτερη ήταν οι δείκτες παγκόσμιας ανάπτυξης.

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Assessing the performance of educational institutions: A multidimensional approach

Cited by 3 publications

References 14 publications

Rankings analysis with the Optimized Pareto method.

Rankings analysis with the Optimized Pareto method.

Visual-aided ontology-based ranking on multidimensional data: a case study in academia

Visual interactive knowledge management for multicriteria decision making and ranking in linked open data environments

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